CN112699205A - Atmospheric visibility forecasting method and device, terminal equipment and readable storage medium - Google Patents
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Abstract
The embodiment of the invention discloses an atmospheric visibility forecasting method, an atmospheric visibility forecasting device, terminal equipment and a readable storage medium, wherein the method comprises the following steps: acquiring a target geographical position and target time of atmospheric visibility to be forecasted; determining weather conditions corresponding to the target geographic position and the target time according to the target geographic position and the target time; determining the cloud droplet particle distribution corresponding to the target geographic position at the target time according to the target geographic position, the target time and the corresponding meteorological conditions by utilizing a pre-trained standard cloud droplet particle distribution model; and forecasting the atmospheric visibility of the target geographic position at the target time according to the cloud fog droplet particle distribution. The technical scheme of the invention realizes the rapid and accurate prediction of the low visibility phenomenon in the local area range and short time interval, and has low hardware cost and easy operation.
Description
Technical Field
The invention relates to the field of weather forecasting, in particular to an atmospheric visibility forecasting method, an atmospheric visibility forecasting device, terminal equipment and a readable storage medium.
Background
In the meteorological field, visibility forecasts are not only used for weather analysis of daily meteorological departments, but also widely used in the fields of traffic transportation departments such as highways, aviation, navigation and the like, military and the like. Visibility is the maximum horizontal distance that a person with normal vision can see and distinguish the outline of an object from the background of the sky under the weather condition. The visibility forecast is a decisive reference basis for judging the visual range obstruction phenomenon and the intensity, the visibility forecast is accurate, and the normal operation of the traffic and transportation industry can be powerfully ensured; on the other hand, is also an important physical quantity for characterizing the degree of pollution of the low atmosphere. Therefore, it is very important to observe good visibility. At present, the forecasting method of atmospheric visibility by a meteorological bureau mainly has the problems of high hardware cost, high operation complexity and the like.
Disclosure of Invention
In view of the above problems, the present invention provides an atmospheric visibility forecasting method, an atmospheric visibility forecasting device, a terminal device and a readable storage medium.
The invention provides an atmospheric visibility forecasting method, which comprises the following steps:
acquiring a target geographical position and target time of atmospheric visibility to be forecasted;
determining weather conditions corresponding to the target geographic position and the target time according to the target geographic position and the target time;
determining the cloud droplet particle distribution corresponding to the target geographic position at the target time according to the target geographic position, the target time and the corresponding meteorological conditions by utilizing a pre-trained standard cloud droplet particle distribution model;
and forecasting the atmospheric visibility of the target geographic position at the target time according to the cloud fog droplet particle distribution.
According to the atmospheric visibility forecasting method, the cloud and fog droplet particle distribution model is trained in advance by the following method:
cleaning and filtering a meteorological training data set used for training the cloud and fog droplet particle distribution model to obtain effective meteorological training samples in the meteorological training data set;
adding a time label, a geographic label and a meteorological label to each effective meteorological training sample;
and training the cloud droplet particle distribution model by using each effective meteorological training sample with the time tag, the geographic tag and the meteorological tag until the loss function corresponding to the cloud droplet particle distribution model converges.
The method for forecasting the atmospheric visibility of the target geographic position at the target time according to the cloud and fog droplet particle distribution comprises the following steps:
determining the corresponding total water content according to the water content of the single spherical cloud droplet particle and the distribution of the cloud droplet particle;
determining an atmospheric extinction coefficient according to the total water content;
and forecasting the atmospheric visibility of the target geographic position at the target time according to the atmospheric extinction coefficient.
According to the atmospheric visibility forecasting method, the total water content is determined by using the following formula:
According to the atmospheric visibility forecasting method, the atmospheric extinction coefficient is determined by using the following formula:
the atmospheric visibility forecasting method of the invention determines the atmospheric visibility by using the following formula:
The invention provides an atmospheric visibility forecasting device, which comprises:
the space-time acquisition module is used for acquiring a target geographical position and target time of the atmospheric visibility to be forecasted;
the weather determining module is used for determining weather conditions corresponding to the target geographic position and the target time according to the target geographic position and the target time;
the distribution determining module is used for determining the cloud droplet particle distribution corresponding to the target geographic position at the target time according to the target geographic position, the target time and the corresponding meteorological condition by utilizing a pre-trained standard cloud droplet particle distribution model;
and the atmosphere forecasting module is used for forecasting the atmospheric visibility of the target geographic position at the target time according to the cloud and fog droplet particle distribution.
According to the atmospheric visibility forecasting device, the cloud droplet particle distribution model is trained in advance by the following method:
cleaning and filtering a meteorological training data set used for training the cloud and fog droplet particle distribution model to obtain effective meteorological training samples in the meteorological training data set;
adding a time label, a geographic label and a meteorological label to each effective meteorological training sample;
and training the cloud droplet particle distribution model by using each effective meteorological training sample with the time tag, the geographic tag and the meteorological tag until the loss function corresponding to the cloud droplet particle distribution model converges.
The invention provides a terminal device, which comprises a memory and a processor, wherein the memory stores a computer program, and the computer program executes the atmospheric visibility forecasting method when running on the processor.
The invention proposes a readable storage medium storing a computer program which, when run on a processor, executes the atmospheric visibility forecasting method according to the invention.
The invention provides an atmospheric visibility forecasting method, which comprises the following steps: acquiring a target geographical position and target time of atmospheric visibility to be forecasted; determining weather conditions corresponding to the target geographic position and the target time according to the target geographic position and the target time; determining the cloud droplet particle distribution corresponding to the target geographic position at the target time according to the target geographic position, the target time and the corresponding meteorological conditions by utilizing a pre-trained standard cloud droplet particle distribution model; and forecasting the atmospheric visibility of the target geographic position at the target time according to the cloud fog droplet particle distribution. The technical scheme of the invention realizes the rapid and accurate prediction of the low visibility phenomenon in the local area range and short time interval, and has low hardware cost and easy operation.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings required to be used in the embodiments will be briefly described below, and it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope of the present invention. Like components are numbered similarly in the various figures.
Fig. 1 is a schematic flow chart of an atmospheric visibility forecasting method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a cloud droplet particle distribution model training method according to an embodiment of the present invention;
FIG. 3 is a flow chart of another atmospheric visibility forecasting method according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating extinction efficiency versus wavelength according to an embodiment of the present invention;
fig. 5 shows a schematic structural diagram of an atmospheric visibility forecasting device according to an embodiment of the present invention.
Description of the main element symbols:
10-atmospheric visibility forecasting device; 11-a space-time acquisition module; 12-a weather determination module; 13-a distribution determination module; 14-atmosphere forecasting module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Hereinafter, the terms "including", "having", and their derivatives, which may be used in various embodiments of the present invention, are only intended to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be construed as first excluding the existence of, or adding to, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. The terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning that is consistent with their contextual meaning in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in various embodiments of the present invention.
The presence of clouds and fog can significantly reduce visibility, considering that when water vapour is close to saturation or supersaturation, the change in visibility depends on scattering and absorption of light by cloud/fog particles suspended in the atmosphere. In addition, different visibility parameterization schemes need to be adopted in the process of numerical simulation in different time periods and seasons. The invention provides an atmospheric visibility forecasting method, which constructs a cloud and fog droplet particle distribution model by machine learning and utilizes three sets of meteorological big data: 1) a ground meteorological station/environmental observation station/satellite observation dataset; 2) numerical simulation forecasting data set; 3) and the GIS terrain data set trains a cloud and fog droplet particle distribution model based on meteorological conditions, time conditions and space conditions, so that the cloud and fog droplet particle distribution model can determine the cloud and fog droplet particle distribution of the target geographic position at the target time according to the target geographic position, the target time and the meteorological conditions corresponding to the target geographic position and the target time, and further forecast the atmospheric visibility of the target geographic position at the target time according to the cloud and fog droplet particle distribution. Meanwhile, different weather conditions in different areas and different underlying surfaces of surrounding environments bring different visibility parametric inputs, the adopted GIS data can reach 10 m/30 m/90 meter resolution, and weather forecast can reach 1km level resolution and 10 minute level intervals, so that the atmospheric visibility forecasting method provided by the invention can quickly judge the low visibility phenomenon in a local area and in a short time interval.
Example 1
In this embodiment, referring to fig. 1, it is shown that an atmospheric visibility forecasting method includes the following steps:
s100: and acquiring the target geographical position and the target time of the atmospheric visibility to be forecasted.
The user can input the target geographical position and the target time of the atmospheric visibility to be forecasted, so that the terminal equipment can acquire the target geographical position and the target time of the atmospheric visibility to be forecasted.
S200: and determining weather conditions corresponding to the target geographic position and the target time according to the target geographic position and the target time.
It can be understood that the weather conditions are related to the geographic position and the season, the weather in different geographic areas at the same time is different, the weather in different time and different seasons in the same geographic area is different, the weather data of different regions and different time in about 1 to 3 years or longer can be counted in advance, and after the target geographic position and the target time of the visibility of the atmosphere to be forecasted are obtained, the weather conditions corresponding to the target geographic position and the target time can be determined.
S300: and determining the cloud droplet particle distribution corresponding to the target geographic position at the target time according to the target geographic position, the target time and the corresponding meteorological conditions by utilizing a pre-trained standard cloud droplet particle distribution model.
By analyzing a large amount of meteorological data, the cloud droplet particle distribution generally conforms to a log-normal distribution. Can set the particle distribution of the cloud and fog drops near the ground
Wherein N isdThe concentration of the number of cloud drops/fog drops, r the particle radius of the cloud drops/fog drops, x ═ ln (r), x0＝ln(r0)，r0Is the geometric median radius, σxIs the width distribution of the logarithmic distribution. The optimal values of all parameters in the cloud droplet particle distribution at different time and space can be determined by utilizing machine learning, and further, a cloud droplet particle distribution model can be obtained. And determining the cloud droplet particle distribution corresponding to the target geographic position at the target time according to the target geographic position, the target time and the corresponding meteorological condition by using a cloud droplet particle distribution model.
S400: and forecasting the atmospheric visibility of the target geographic position at the target time according to the cloud fog droplet particle distribution.
If the cloud droplet distribution n (r) is known, a relationship can be established between the micro-physical structure of the cloud and its optical properties, in particular between the cloud water content (water content) and the extinction coefficient. Then, based on the extinction coefficient, the atmospheric visibility of the target geographic location at the target time can be determined using the horizontal atmospheric visibility Koschmieder's law.
The embodiment provides an atmospheric visibility forecasting method, which includes: acquiring a target geographical position and target time of atmospheric visibility to be forecasted; determining weather conditions corresponding to the target geographic position and the target time according to the target geographic position and the target time; determining the cloud droplet particle distribution corresponding to the target geographic position at the target time according to the target geographic position, the target time and the corresponding meteorological conditions by utilizing a pre-trained standard cloud droplet particle distribution model; and forecasting the atmospheric visibility of the target geographic position at the target time according to the cloud fog droplet particle distribution. The technical scheme of the invention realizes the rapid and accurate prediction of the low visibility phenomenon in the local area range and short time interval, and has low hardware cost and easy operation.
Example 2
This embodiment, referring to fig. 2, shows that training of a cloud droplet particle distribution model includes the following steps:
s310: and cleaning and filtering a meteorological training data set used for training the cloud and fog droplet particle distribution model to obtain effective meteorological training samples in the meteorological training data set.
The cloud droplet particle distribution model can be determined by utilizing a decision tree algorithm, a neural network algorithm or a support vector machine and other components, and then the level and the parameters of the cloud droplet particle distribution model are determined through training.
The meteorological training data set comprises ground meteorological station/environment observation station/satellite observation data, numerical simulation forecast data and GIS terrain data.
The ground meteorological station data comprise temperature, humidity, pressure, dew point temperature, precipitation, visibility, wind speed and wind direction; the environmental station data includes temperature, humidity, cloud droplet (fog droplet) particle number concentration, PM2.5, PM10, sulfate, nitrate, NO2、SO2(ii) a The satellite observation data comprises clear sky detection, cloud/fog detection, the whole-layer optical thickness of the cloud/fog, atmospheric temperature and humidity profiles, atmospheric motion wind guide and surface temperature.
The numerical simulation forecast data subset comprises: temperature, humidity, pressure, dew point temperature, precipitation, visibility, wind speed, wind direction, static stability, cloud water content, cloud/fog optical thickness, cloud/fog backscattering, PM2.5, PM10, sulfate, nitrate, NO2、SO2And the like.
The GIS terrain data subset comprises: latitude and longitude, altitude, digital surface data, digital elevation data, vegetation, elevated roads, bridges, tunnels, and the like.
Data in a meteorological training dataset needs to be cleaned and filtered to obtain valid meteorological training samples in the meteorological training dataset.
S320: and adding a time label, a geographic label and a weather label to each effective weather training sample.
It is understood that the absolute value of the difference between the numbers of valid weather training samples corresponding to different time tags should be less than or equal to a predetermined difference threshold, the absolute value of the difference between the numbers of valid weather training samples corresponding to different geographic tags should be less than or equal to a predetermined difference threshold, and the predetermined difference threshold is preferably 0.
S330: and training the cloud droplet particle distribution model by using each effective meteorological training sample with the time tag, the geographic tag and the meteorological tag until the loss function corresponding to the cloud droplet particle distribution model converges.
And training the cloud droplet particle distribution model by using each effective meteorological training sample with the time tag, the geographic tag and the meteorological tag, and continuously optimizing each parameter in the cloud droplet particle distribution model until the loss function corresponding to the cloud droplet particle distribution model is converged.
Example 3
In this embodiment, referring to fig. 3, the method for forecasting atmospheric visibility according to the cloud droplet particle distribution includes the following steps:
s410: and determining the corresponding total water content according to the cloud droplet particle distribution and the water content of the single spherical cloud droplet particle.
It will be appreciated that in the absence of precipitation, atmospheric visibility is typically affected by clouds or fog, from which cloud-fog droplet particle distributions can be determined.
Further, the total water content corresponding to the cloud droplet particle distribution at the target geographic location at the target time is determined using the following formula:
S420: and determining the atmospheric extinction coefficient according to the total water content.
For non-precipitation cloud/droplet particles (particle radius of 3-20 μm), the extinction efficiency of a single spherical cloud droplet particle can be defined as Q according to the Mie scattering theoryext(r, λ, m), r is the radius of the individual spherical cloud droplet particles, λ is the wavelength of the incident light wave, and m is the medium complex refractive index. It is understood that the extinction efficiency is determined by the radius of the single spherical cloud droplet particle, the wavelength of the incident light wave, and the complex refractive index of the medium, and further, the particle size α is 2 π r/λ defined by the radius of the single spherical cloud droplet particle and the wavelength of the incident light wave, as shown in FIG. 4, the horizontal axis is the particle size, and the vertical axis is the particle scattering extinction efficiency. The complex refractive index m ═ n + i ×, k, where n is the refractive index of the absorbing medium, determining the propagation speed of light in the absorbing medium; k is the absorption coefficient, which determines the attenuation of light as it propagates through an absorbing medium. As the particle size increases, the meter scattering extinction efficiency has been substantially constant with wavelength, indicating that the various wavelengths have nearly the same intensity of scattering, then Qext(r，λ，m)≈2。
Further, if the cloud droplet particle distribution n (r) is known, a relationship between the micro-physical structure of the cloud and its optical properties, i.e. the extinction efficiency Q of a single spherical cloud droplet particle, can be establishedext(r, λ, m), the cloud droplet particle distribution and the atmospheric extinction coefficient can be expressed by the following formulas.
Further in accordance with
The atmospheric extinction coefficient corresponding to the cloud and fog droplet particle distribution corresponding to the target geographic position at the target time can be determined
S430: and forecasting the atmospheric visibility of the target geographic position at the target time according to the atmospheric extinction coefficient.
Further, the atmospheric visibility of the target geographic position at the target time can be determined by utilizing the Koschmieder law of horizontal atmospheric visibility
ε represents the visual response threshold and V represents the atmospheric visibility.
Further, can be
In (1)
Is defined as the effective radius reThen, then
If the visual response threshold ε is 0.02, then
Example 4
In the present embodiment, referring to fig. 5, an atmospheric visibility forecasting apparatus 10 is shown including: a space-time acquisition module 11, a weather determination module 12, a distribution determination module 13, and an atmospheric forecast module 14.
The space-time acquisition module 11 is used for acquiring a target geographical position and a target time of the atmospheric visibility to be forecasted; a weather determining module 12, configured to determine, according to the target geographic position and the target time, a weather condition distribution determining module 13 corresponding to the target geographic position and the target time, and determine, by using a pre-trained standard cloud droplet particle distribution model, cloud droplet particle distribution corresponding to the target geographic position at the target time according to the target geographic position, the target time, and the corresponding weather condition; and the atmosphere forecasting module 14 is used for forecasting the atmospheric visibility of the target geographic position at the target time according to the cloud and fog droplet particle distribution.
Further, the cloud droplet particle distribution model is pre-trained by using the following method:
cleaning and filtering a meteorological training data set used for training the cloud and fog droplet particle distribution model to obtain effective meteorological training samples in the meteorological training data set; adding a time label, a geographic label and a meteorological label to each effective meteorological training sample; and training the cloud droplet particle distribution model by using each effective meteorological training sample with the time tag, the geographic tag and the meteorological tag until the loss function corresponding to the cloud droplet particle distribution model converges.
Further, the forecasting atmospheric visibility of the target geographic location at the target time according to the cloud droplet particle distribution includes:
determining the corresponding total water content according to the water content of the single spherical cloud droplet particle and the distribution of the cloud droplet particle; determining an atmospheric extinction coefficient according to the total water content; and forecasting the atmospheric visibility of the target geographic position at the target time according to the atmospheric extinction coefficient.
Further, in the atmospheric visibility forecasting method, the total moisture content is determined by using the following formula:
Further, the atmospheric extinction coefficient is determined using the following equation:
further, the atmospheric visibility is determined using the following formula:
The atmospheric visibility forecasting apparatus 10 provided in this embodiment is used to execute the atmospheric visibility forecasting method described in the above embodiment by using the space-time obtaining module 11, the weather determining module 12, the distribution determining module 13, and the atmospheric forecasting module 14 in a matching manner, and the implementation and the beneficial effects related to the above embodiment are also applicable in this embodiment, and are not described herein again.
It is understood that the present invention provides a terminal device, which includes a memory and a processor, where the memory stores a computer program, and the computer program executes the atmospheric visibility forecasting method according to the present invention when running on the processor.
It is to be understood that the present invention proposes a readable storage medium storing a computer program which, when run on a processor, executes the atmospheric visibility forecasting method according to the present invention.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, each functional module or unit in each embodiment of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part of the technical solution that contributes to the prior art in essence can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (10)
1. An atmospheric visibility forecasting method, characterized in that the method comprises:
acquiring a target geographical position and target time of atmospheric visibility to be forecasted;
determining weather conditions corresponding to the target geographic position and the target time according to the target geographic position and the target time;
determining the cloud droplet particle distribution corresponding to the target geographic position at the target time according to the target geographic position, the target time and the corresponding meteorological conditions by utilizing a pre-trained standard cloud droplet particle distribution model;
and forecasting the atmospheric visibility of the target geographic position at the target time according to the cloud fog droplet particle distribution.
2. The atmospheric visibility forecasting method according to claim 1, wherein the cloud droplet particle distribution model is pre-trained using:
cleaning and filtering a meteorological training data set used for training the cloud and fog droplet particle distribution model to obtain effective meteorological training samples in the meteorological training data set;
adding a time label, a geographic label and a meteorological label to each effective meteorological training sample;
and training the cloud droplet particle distribution model by using each effective meteorological training sample with the time tag, the geographic tag and the meteorological tag until the loss function corresponding to the cloud droplet particle distribution model converges.
3. The atmospheric visibility forecasting method according to claim 1, wherein the forecasting of the atmospheric visibility of the target geographic location at the target time according to the cloud droplet particle distribution includes:
determining the corresponding total water content according to the water content of the single spherical cloud droplet particle and the distribution of the cloud droplet particle;
determining an atmospheric extinction coefficient according to the total water content;
and forecasting the atmospheric visibility of the target geographic position at the target time according to the atmospheric extinction coefficient.
4. An atmospheric visibility forecasting method according to claim 3, characterized in that said total moisture content is determined using the following formula:
5. The atmospheric visibility forecasting method according to claim 4, wherein the atmospheric extinction coefficient is determined by using the following formula:
6. the atmospheric visibility forecasting method according to claim 5, wherein the atmospheric visibility is determined using the following formula:
7. An atmospheric visibility forecasting apparatus, characterized in that the apparatus comprises:
the space-time acquisition module is used for acquiring a target geographical position and target time of the atmospheric visibility to be forecasted;
the weather determining module is used for determining weather conditions corresponding to the target geographic position and the target time according to the target geographic position and the target time;
the distribution determining module is used for determining the cloud droplet particle distribution corresponding to the target geographic position at the target time according to the target geographic position, the target time and the corresponding meteorological condition by utilizing a pre-trained standard cloud droplet particle distribution model;
and the atmosphere forecasting module is used for forecasting the atmospheric visibility of the target geographic position at the target time according to the cloud and fog droplet particle distribution.
8. The atmospheric visibility forecasting device according to claim 7, wherein the cloud droplet particle distribution model is pre-trained using the following method:
cleaning and filtering a meteorological training data set used for training the cloud and fog droplet particle distribution model to obtain effective meteorological training samples in the meteorological training data set;
adding a time label, a geographic label and a meteorological label to each effective meteorological training sample;
and training the cloud droplet particle distribution model by using each effective meteorological training sample with the time tag, the geographic tag and the meteorological tag until the loss function corresponding to the cloud droplet particle distribution model converges.
9. A terminal device, characterized in that it comprises a memory and a processor, said memory storing a computer program which, when run on said processor, executes the atmospheric visibility forecasting method according to any one of claims 1 to 6.
10. A readable storage medium, characterized in that it stores a computer program which, when run on a processor, performs the atmospheric visibility forecasting method of any one of claims 1 to 6.
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